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The effects of rain and evapotranspiration statistics on groundwater recharge estimations for semi-arid environments
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The effects of rain and evapotranspiration statistics on groundwater recharge estimations for semi-arid environments
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Journal Article
The effects of rain and evapotranspiration statistics on groundwater recharge estimations for semi-arid environments
2023
Overview
A better understanding the effects of rainfall and evapotranspiration statistics on groundwater recharge (GR) requires long time series of these variables. However, long records of the relevant variables are scarce. To overcome this limitation, time series of rainfall and evapotranspiration are often synthesized using different methods. Here, we attempt to study the dependence of estimated GR on the synthesis methods used. We focus on regions with semi-arid climate conditions and soil types. For this purpose, we used longer than 40 year records of the daily rain and climate variables that are required to calculate the potential evapotranspiration (ETref), which were measured in two semi-arid locations.These locations, Beit Dagan and Shenmu, have aridity indices of 0.39 and 0.41, respectively, and similar seasonal and annual ETref rates (1370 and 1030 mm yr−1, respectively) but different seasonal rain distributions. Stochastic daily rain and ETref time series were synthesized according to the monthly empirical distributions. This synthesis method does not preserve the monthly and annual rain and ETref distributions. Therefore, we propose different correction methods to match the synthesized and measured time series' annual or monthly statistics. GR fluxes were calculated using the 1D Richards equation for four typical semi-arid soil types, and by prescribing the synthesized rain and ETref as atmospheric conditions. The estimated GR fluxes are sensitive to the synthesis method. However, the ratio between the GR and the total rain does not show the same sensitivity. The effects of the synthesis methods are shown to be the same for both locations, and correction of the monthly mean and SD of the synthesized time series results in the best agreement with independent estimates of the GR. These findings suggest that the assessment of GR under current and future climate conditions depends on the synthesis method used for rain and ETref.
